A comparative study of conventional and deep learning approaches for demosaicing Mastcam images

Chiman Kwan; Bryan Chou

doi:10.1117/12.2518489

7 May 2019 A comparative study of conventional and deep learning approaches for demosaicing Mastcam images

Chiman Kwan, Bryan Chou

Proceedings Volume 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; 1101814 (2019) https://doi.org/10.1117/12.2518489
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Bayer pattern is a low cost approach to generating RGB images in commercial digital cameras. In NASA's mast camera (Mastcams) onboard the Mars rover Curiosity, Bayer pattern has also been used in capturing the RGB bands. It is well known that debayering (also known as demosaicing) introduces color and zipper artifacts. Currently, NASA is using a demosaicing algorithm developed in early 2000’s. It is probably the right time to assess some state-of-the-art algorithms and recommend a more recent and powerful approach to NASA for its future missions. In this paper, we present results of a comparative study on the use of conventional and deep learning algorithms for demosaicing Mastcam images. Due to lack of ground truth, subjective evaluation has been used in our study.

Conference Presentation

Citation Download Citation

Chiman Kwan and Bryan Chou "A comparative study of conventional and deep learning approaches for demosaicing Mastcam images", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 1101814 (7 May 2019); https://doi.org/10.1117/12.2518489

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